面向车联网的车辆节点信誉评估方法

doi:10.3969/j.issn.1671-1122.2024.05.006

信息网络安全 ›› 2024, Vol. 24 ›› Issue (5): 719-731.doi: 10.3969/j.issn.1671-1122.2024.05.006

面向车联网的车辆节点信誉评估方法

田钊¹^,²^,³, 牛亚杰¹^,³, 佘维¹^,³^,⁴, 刘炜¹^,²^,³()

1.郑州大学网络空间安全学院，郑州 450002
2.河南省网络密码技术重点实验室，郑州 450002
3.郑州市区块链与数据智能重点实验室，郑州 450002
4.嵩山实验室，郑州 450002

收稿日期:2023-11-25 出版日期:2024-05-10 发布日期:2024-06-24
通讯作者: 刘炜 E-mail:wliu@zzu.edu.cn
作者简介:田钊（1985—），男，河南，副教授，博士，CCF会员，主要研究方向为信息安全、区块链、智能交通|牛亚杰（1998—），女，河南，硕士研究生，主要研究方向为区块链、智能交通|佘维（1977—），男，湖南，教授，博士，CCF会员，主要研究方向为信息安全、区块链、隐蔽通信|刘炜（1981—），男，河南，副教授，博士，CCF会员，主要研究方向为信息安全、区块链、智慧医疗
基金资助:
河南省高等学校重点科研项目(24A520045);河南省网络密码技术重点实验室开放课题(LNCT2022-A04);嵩山实验室预研项目(YYYY022022003)

A Reputation Evaluation Method for Vehicle Nodes in V2X

TIAN Zhao¹^,²^,³, NIU Yajie¹^,³, SHE Wei¹^,³^,⁴, LIU Wei¹^,²^,³()

1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China
2. Henan Key Laboratory of Network Cryptography Technology, Zhengzhou 450002, China
3. Zhengzhou Key Laboratory of Blockchain and Data Intelligence, Zhengzhou 450002, China
4. SongShan Laboratory, Zhengzhou 450002, China

Received:2023-11-25 Online:2024-05-10 Published:2024-06-24
Contact: LIU Wei E-mail:wliu@zzu.edu.cn

摘要/Abstract

摘要：

车联网技术的发展促使了交通信息的交互与共享，能有效提升出行效率，但车联网的开放性使得交通实体容易受到恶意车辆的攻击，会造成严重的后果。针对上述问题，文章提出了一种面向车联网的车辆节点信誉评估方法。首先，提出了一种面向车联网的车辆节点分区区块链网络；然后，通过车辆节点之间的信任和基础设施对车辆的辅助信任来计算本地信誉值，借助深度学习算法动态计算全局信誉值，进而可基于全局信誉值确定最佳的数据共享节点；最后，对存储技术进行改进，利用分区区块链存储来保证信誉值、路况信息的不被篡改和可追溯性。仿真实验结果表明，本文所提的方法在通过信誉评估确定恶意节点的准确率和召回率都优于对比方法。

关键词: 车联网, 信誉评估, 区块链, 深度学习

Abstract:

The advancements in Vehicular Networks communication technologies facilitate the exchange and sharing of traffic information, thereby significantly enhancing travel efficiency. However, the openness of V2X networks increases the vulnerability of traffic entities to attacks from malicious vehicles, potentially leading to severe consequences. Addressing this issue, this paper proposed a reputation evaluation method for vehicle nodes in V2X. Initially, a partitioned blockchain network for vehicle nodes in Vehicular Networks was introduced. Subsequently, local reputation values were calculated based on trust among vehicle nodes and auxiliary trust from infrastructures, combined with the use of deep learning for dynamically computing global reputation values. This enabled the identification of optimal data sharing nodes based on global reputation scores. Finally, to enhance storage technology, partitioned blockchain technology was employed to ensure the integrity and traceability of reputation values and traffic information. Simulation results demonstrated that the proposed method outperformed comparative methods in accurately identifying malicious nodes, as evidenced by higher precision and recall rates.

Key words: V2X, reputation evaluation, blockchain, deep learning

中图分类号:

TP309

田钊, 牛亚杰, 佘维, 刘炜. 面向车联网的车辆节点信誉评估方法[J]. 信息网络安全, 2024, 24(5): 719-731.

TIAN Zhao, NIU Yajie, SHE Wei, LIU Wei. A Reputation Evaluation Method for Vehicle Nodes in V2X[J]. Netinfo Security, 2024, 24(5): 719-731.

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参考文献 20

[1]	ZHAO Junhui, HE Lin, ZHANG Danyang, et al. A TP-DDPG Algorithm Based on Cache Assistance for Task Offloading in Urban Rail Transit[J]. IEEE Transactions on Vehicular Technology, 2023, 72(8): 1-11.
[2]	LI Bohan, LIANG Ruochen, ZHU Di, et al. Blockchain-Based Trust Management Model for Location Privacy Preserving in VANET[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(6): 3765-3775.
[3]	ZHAO Junhui, SUN Xiaoke, MA Xiaoting, et al. Online Distributed Optimization for Energy-Efficient Computation Offloading in Air-Ground Integrated Networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(4): 5110-5124.
[4]	YANG Qing, ZHU Binghai, WU Shaoen. An Architecture of Cloud-Assisted Information Dissemination in Vehicular Networks[J]. IEEE Access, 2016, 4: 2764-2770.
[5]	ZHANG Kuan, NI Jianbing, YANG Kan, et al. Security and Privacy in Smart City Applications: Challenges and Solutions[J]. IEEE Communications Magazine, 2017, 55(1): 122-129.
[6]	LIU Zhiquan, HUANG Feiran, WENG Jian, et al. BTMPP: Balancing Trust Management and Privacy Preservation for Emergency Message Dissemination in Vehicular Networks[J]. IEEE Internet of Things Journal, 2021, 8(7): 5386-5407.
[7]	HUANG Fanwei, LI Qiuping, ZHAO Junhui. Trust Management Model of VANETs Based on Machine Learning and Active Detection Technology[C]// IEEE. 2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops). New York: IEEE, 2022: 412-416.
[8]	LIU Jiawei, MA Zhaofeng, WANG Shushuang, et al. Research on the Restricted Sharing Technology of Private Credit Data Based on Blockchain[J]. Netinfo Security, 2022, 22(5): 54-63.
	刘嘉微, 马兆丰, 王姝爽, 等. 基于区块链的隐私信用数据受限共享技术研究[J]. 信息网络安全, 2022, 22(5): 54-63.
[9]	MOLLAH M B, ZHAO Jun, NIYATO D, et al. Blockchain for the Internet of Vehicles Towards Intelligent Transportation Systems: A Survey[J]. IEEE Internet of Things Journal, 2021, 8(6): 4157-4185.
[10]	TANGADE S, MANVI S. A Survey on Attacks, Security and Trust Management Solutions in VANETs[C]// IEEE. 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). New York: IEEE, 2013: 1-6.
[11]	LI Qin, MALIP A, MARTIN K M, et al. A Reputation-Based Announcement Scheme for VANETs[J]. IEEE Transactions on Vehicular Technology, 2012, 61(9): 4095-4108.
[12]	CHEN Liqun, LI Qin, MARTIN K M. A Privacy-Aware Reputation-Based Announcement Scheme for VANETs[C]// IEEE. 2013 IEEE 5th International Symposium on Wireless Vehicular Communications (WIVEC). New York: IEEE, 2013: 1-13.
[13]	HALABI T, ZULKERNINE M. Trust-Based Cooperative Game Model for Secure Collaboration in the Internet of Vehicles[C]// IEEE. 2019 IEEE International Conference on Communications (ICC). New York: IEEE, 2019: 1-6.
[14]	ZHANG Dajun, Yu F R, YANG Ruizhe. A Machine Learning Approach for Software-Defined Vehicular Ad Hoc Networks with Trust Management[C]// IEEE. 2018 IEEE Global Communications Conference. New York: IEEE, 2018: 1-6.
[15]	GYAWALI S, QIAN Y, HU R Q. Deep Reinforcement Learning Based Dynamic Reputation Policy in 5G Based Vehicular Communication Networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70(6): 6136-6146.
[16]	YANG Zhe, YANG Kan, LEI Lei, et al. Blockchain-Based Decentralized Trust Management in Vehicular Networks[J]. IEEE Internet of Things Journal, 2019, 6(2): 1495-1505. doi: 10.1109/JIOT.2018.2836144
[17]	DONG Wenbin, LI Yang, HOU Ronghui, et al. A Blockchain-Based Hierarchical Reputation Management Scheme in Vehicular Network[C]// IEEE. 2019 IEEE Global Communications Conference (GLOBECOM). New York: IEEE, 2019: 1-6.
[18]	AHMED K, ANDREW M, SHI E, et al. Hawk: The Blockchain Model of Cryptography and Privacy-Preserving Smart Contracts[C]// IEEE. 2016 IEEE Symposium on Security and Privacy (SP). New York: IEEE, 2016: 839-858.
[19]	CHEN I, BAO F, CHANG M, et al. Dynamic Trust Management for Delay Tolerant Networks and Its Application to Secure Routing[J]. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(5): 1200-1210.
[20]	MAO Ming, YI Peng, HU Tao, et al. Hierarchical Hybrid Trust Management Scheme in SDN-Enabled VANETs[J]. Mobile Information Systems, 2021, 1-12.

输入	描述
$L{{T}_{ij}}$	由数据请求者${{v}_{i}}$计算的数据提供者${{v}_{j}}$的本地信誉值
$GT_{ij}^{t-\Delta t}$	数据提供者${{v}_{j}}$前一时段历史信誉值
$GT_{ij}^{t-2\Delta t}$	数据提供者${{v}_{j}}$前两个时段的历史信誉值
$GT_{ij}^{t-3\Delta t}$	数据提供者${{v}_{i}}$前三个时段的历史信誉值
$attac{{k}_{j}}$	数据提供者${{v}_{j}}$充当恶意节点累积攻击次数
${{x}_{i}}$	数据请求者${{v}_{i}}$当前横坐标
${{y}_{i}}$	数据请求者${{v}_{i}}$当前纵坐标
${{x}_{j}}$	数据提供者${{v}_{j}}$当前横坐标
${{y}_{j}}$	数据提供者${{v}_{j}}$当前纵坐标
${{x}_{\text{RSU}}}$	数据请求者${{v}_{i}}$所属RSU的横坐标
${{y}_{\text{RSU}}}$	数据请求者${{v}_{i}}$所属RSU的纵坐标

参数名称	参数数值
仿真时间/h	12
车辆节点数量/个	300
恶意节点占比	[0,50%]
车辆节点速度/m/s	[5,20]
车辆无线传输范围/m	[0,200]
信任阈值$G{{T}_{th1}}$、$G{{T}_{th2}}$	0.5、0.8
权重${{\alpha }_{1}}$、${{\alpha }_{2}}$、${{\alpha }_{3}}$	0.5、0.4、0.1
权重${{\beta }_{1}}$、${{\beta }_{2}}$	0.3、0.7
权重${{\psi }_{1}}$、${{\psi }_{2}}$、${{\psi }_{3}}$	0.4、 0.3、0.3

面向车联网的车辆节点信誉评估方法

A Reputation Evaluation Method for Vehicle Nodes in V2X

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